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Enterprise Agents

Enterprise AI agent development for the systems you run

dotSolved.ai is an AI agent development company for enterprise ERP and CRM. We implement the native AI inside NetSuite, Oracle Fusion, Salesforce and Microsoft, build custom agents on Claude and MCP where those platforms stop, and govern every agent with the permissions, definitions and audit trail your finance and IT teams already enforce.

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What we do

What does an AI agent development company do?

An AI agent development company designs, builds and operates software agents that complete business tasks inside your systems, rather than only answering questions about them. For an enterprise running NetSuite, Oracle Fusion, Salesforce or Microsoft Dynamics 365, that work has three parts: implementing the AI the platform already ships, building the agents it does not, and governing both so an agent that can post a journal, update an opportunity or release a payment does so inside the controls your auditors expect. dotSolved.ai does all three, drawing on 23+ years of implementing enterprise ERP and CRM platforms and on Nexatron, our data intelligence product that gives agents and MCP clients such as Claude governed access to ERP and CRM data.

  • Implement native platform AI

    NetSuite Next, Oracle Fusion Agentic Applications, Salesforce Agentforce and Microsoft Copilot Studio, configured for your subsidiaries, roles and approval flows instead of switched on with defaults.

  • Build custom agents where the platform stops

    Agents built on Claude, the Claude Agent SDK and the Model Context Protocol for the workflows native AI leaves open: cash forecasting, close orchestration, reconciliation, revenue signals and cross-system operations.

  • Govern and operate every agent

    An identity for each agent, row-level permissions, certified metric definitions, approval gates for any action that writes to a system of record, and a log of what the agent read and did.

Implement and build

What do we implement natively, and what do we build?

We implement the AI each platform ships first, and build custom agents only where it stops. Pick a platform to see what it offers natively, the agents dotSolved.ai builds on top, and the kind of question each agent answers.

What Anthropic offers natively

  • The Claude model family (Opus, Sonnet and Haiku), with context windows of up to 1M tokens on supported models; the current lineup is published at anthropic.com
  • Model Context Protocol (MCP), the open standard for connecting agents to enterprise systems
  • Claude Agent SDK and Claude Code for building and running production agents
  • Claude on Amazon Bedrock, Google Cloud, and Microsoft Foundry for in-region enterprise deployment

What dotSolved.ai builds on top

Governed Claude Gateway

Problem: Teams adopt Claude team by team, with no central view of what data leaves the enterprise, what each workload costs, or who approved it

What it does: A single governed entry point for every Claude workload: model routing by task and cost, prompt and response audit trails, data-loss controls, and per-department chargeback

MCP Connector Suite for Enterprise Platforms

Problem: Claude reasons well but cannot reach Oracle, NetSuite, or Salesforce data without someone exporting it first, which is exactly what security will not approve

What it does: Governed MCP servers that expose ERP and CRM data to Claude as read-only tools, enforcing your existing row-level and field-level security on every request

Example questions and illustrative answers

“Can Claude answer questions about our Oracle data without copying it out?”

Yes. MCP exposes governed, read-only tools. The data stays in your systems; only the results of each governed query reach the model.

“Which Claude model should each workload run on?”

The gateway routes by complexity and cost: Opus for deep reasoning, Sonnet for volume, Haiku for latency-sensitive calls.

Answers shown are illustrative. In production each agent answers from your data, with the query and the policies applied.

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Process

How does enterprise AI agent development work?

Every engagement runs through four stages: discover, build, govern and operate. Each stage has a defined output, and no agent reaches production before the govern stage is signed off by the business owner and IT.

  1. 01

    Discover

    We map the workflow the agent will own, the systems and records it must read or change, who approves its actions and how success will be measured. The output is one scoped first agent with a named business owner, not a list of ideas.

  2. 02

    Build

    We implement native platform AI where it covers the need and build custom agents on Claude, the Claude Agent SDK and MCP where it does not. Agents reach ERP and CRM data through governed tools, never through exports.

  3. 03

    Govern

    Before an agent acts in production it gets an identity, row-level and field-level permissions, certified metric definitions, approval gates for anything that writes to a system of record, and a log of every read and action.

  4. 04

    Operate

    We run the agent through a supervised pilot, measure accuracy, exceptions and cycle time against the baseline set in discovery, then hand over runbooks, monitoring and a change process your team owns.

Platforms

Which platforms do we build enterprise AI agents on?

We build and implement agents on NetSuite, Oracle Fusion, Salesforce, Microsoft and Claude, backed by 23+ years of enterprise ERP and CRM implementation work. Each platform page covers the native AI, the agents we build and the questions they answer.

Need governed AI analytics rather than agents? See Solutions for NetSuite, Oracle, Salesforce and Claude, or Nexatron for conversational analytics on ERP and CRM data.

Implementation depth

Why implementation expertise matters

Every platform is adding AI natively. But deploying AI features in a real enterprise, with real data, real security requirements, and real users, is a different problem than enabling a toggle. We have delivered 2,000+ implementations for 500+ enterprises across these platforms. We know where the gaps are because we have lived in them.

  • 23+

    Years of platform depth

    Oracle, NetSuite, Salesforce, Microsoft

  • 2,000+

    Enterprise implementations

    For 500+ enterprises in High-Tech, Financial Services, Healthcare, Retail, Insurance and Energy

  • 5

    Certified partnerships

    Anthropic, Oracle, NetSuite, Salesforce, Microsoft

Guides

Guides for teams planning enterprise AI agents

Start with the platform change that affects you, then the governance work that lets an agent act. Printable versions of the AI governance framework template and the Claude connector approval checklist are available in Resources.

FAQ

Enterprise AI agent development FAQ

What does an AI agent development company do?
An AI agent development company designs, builds, governs and operates software agents that complete tasks inside business systems. dotSolved.ai does this for enterprises on NetSuite, Oracle Fusion, Salesforce, Microsoft Dynamics 365 and Claude: we implement the AI each platform ships, build custom agents on Claude and MCP where the platform stops, and put permissions, certified definitions and an audit trail around both.
Which platforms do you build AI agents on?
NetSuite, Oracle Fusion, Salesforce, Microsoft (Dynamics 365, Copilot Studio and Power Platform) and Anthropic's Claude. Agents reach data through governed tools rather than exports. Nexatron, our data intelligence product, exposes NetSuite, Oracle Fusion, Salesforce, Dynamics 365 and warehouse data to any MCP client, including Claude, ChatGPT and Copilot, with the query, the certified metric definition and the row-level policies carried on every answer.
How do you govern an agent that can act?
Every agent gets its own identity, the same row-level and field-level permissions as the person it works for, certified metric definitions for anything it calculates, and an approval gate before any action that writes to a system of record, such as posting a journal or releasing a payment. Each read and action is logged with the query, the definition used and the policy applied, so finance and audit can replay what the agent did.
How long does a first agent take?
It depends on the workflow, the systems involved and your security review, so we set the timeline in discovery rather than quoting a generic number. We scope the first agent to a single workflow with a named owner, defined data sources and an agreed approval path, which keeps discovery, build, governance review and a supervised pilot inside one planning cycle. Larger programs add agents after the first one is in production.
Do you implement native platform AI too?
Yes. We implement NetSuite Next and Intelligent Close Manager, Oracle Fusion Agentic Applications and AI Agent Studio, Salesforce Agentforce, and Microsoft Dynamics 365 AI and Copilot Studio, configured for your subsidiaries, roles and approval flows. We build custom agents only where the native capability stops, so you are not paying to rebuild what the platform already provides.
How do Claude and MCP fit into enterprise AI agents?
Claude supplies the reasoning, and the Model Context Protocol (MCP) is the open standard that connects it to your systems. We build governed MCP servers that expose ERP and CRM data to Claude as read-only, policy-enforced tools, and use the Claude Agent SDK for agents that plan and act across several steps. Claude runs on the Claude API or inside Amazon Bedrock, Google Cloud or Microsoft Foundry, so inference stays in the cloud you already contract with.

Scope your first enterprise AI agent

Bring one workflow, the systems it touches and the person who will own it. We will map the agent, its permissions and its approval path in the first conversation.

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Nexatron by dotSolved Inc

Connect Claude to business data through Nexatron

See how supported applications connect to Nexatron and how Claude accesses its analytical tools through MCP. Review the setup, permissions and source scope before your first question.

Explore Nexatron's Claude connection 

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